research paper-College student substance abuse

Addictive Behaviors 38 (2013) 2607–2618

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Addictive Behaviors

Prevention and treatment of college student drug use: A review of the literature

Ashley A. Dennhardt, James G. Murphy ⁎ University of Memphis, 202 Psychology Building, Memphis, TN 38152, United States

H I G H L I G H T S

• Approximately 36% of all college students report drug use in the past year. • We review the literature on prevention and intervention of college student drug use. • Brief counselor-delivered motivational interventions may be effective. • The combination of individual and parent-based approaches may also be effective. • Prevention should address social/cognitive, personality and environmental factors.

⁎ Corresponding author. Tel.: +1 901 678 2630. E-mail addresses: [email protected] (A.A. Den

0306-4603/$ – see front matter © 2013 Elsevier Ltd. All http://dx.doi.org/10.1016/j.addbeh.2013.06.006

a b s t r a c t

a r t i c l e i n f o

Keywords:

Substance use Drug use College Prevention Intervention

Drug use during the college years is a significant public health concern. The primary goal of this paper is to provide a comprehensive review of prevention and treatment studies of college student drug use in order to guide college prevention efforts and to inform and stimulate new research in this area. First, established risk factors for drug use were reviewed. High levels of personality traits including, impulsivity, sensation-seeking, negative emotionality, emotional dysregulation, and personality disorder symptoms in- crease risk for drug use. Drug use has also been linked to overestimating normative levels of drug use and experiencing negative life events, and specific motives for drug use are linked to more problematic patterns. There have been very few studies examining prevention and treatment, but parent-based and in-person brief motivational interventions appear to be promising. Longitudinal studies of the development and course of drug use among college students, as well as clinical trials to evaluate novel theoretically-based intervention and prevention programs that take into account established risk factors for drug abuse are needed.

© 2013 Elsevier Ltd. All rights reserved.

Contents

1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2608 1.1. Epidemiology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2608

1.1.1. Prevalence of drug use in college students . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2608 1.1.2. Drug-related consequences in college students . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2609

2. Psychosocial factors related to college student drug use. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2609 2.1. Demographic and lifestyle factors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2609 2.2. Personality and psychiatric comorbidity. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2609 2.3. Social, cognitive, peer, and family influence on drug use . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2610

2.3.1. Social influences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2610 2.3.2. Drug use norms, motives and expectancies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2610

3. Drug use prevention studies for college students . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2611 3.1. Uncontrolled studies. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2611 3.2. Controlled studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2611

4. Drug use intervention studies for college students . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2613 4.1. Uncontrolled studies. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2613 4.2. Controlled studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2613

nhardt), [email protected] (J.G. Murphy).

rights reserved.

2608 A.A. Dennhardt, J.G. Murphy / Addictive Behaviors 38 (2013) 2607–2618

5. Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2614 6. Future directions in college drug use prevention and intervention . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2614 Role of funding sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2615 Contributors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2615 Conflict of interest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2615 Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2615 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2615

1. Introduction

Nationwide surveys reveal that rates of illicit drug use peak in ado- lescence and young adulthood and that college students account for ap- proximately 50% of this high-risk group (SAMHSA, 2010). Alcohol and illicit drug use among college students are major public health prob- lems. There has been extensive research on alcohol use in college stu- dents, including recent review papers (e.g., Carey, Scott-Sheldon, Carey, & DeMartini, 2007; Cronce & Larimer, 2011; Ham & Hope, 2003), but drug use prevention and treatment has received less atten- tion, especially for drugs other than marijuana. The primary goal of this paper is to provide a comprehensive review of prevention and treatment studies of college student illicit drug use in order to inform and stimulate new research in this area. Because effective prevention and intervention approaches should take into account the factors that may contribute to drug use, this paper will begin with a review of established risk factors in college students. Although non-college students are also at high risk for drug use, these populations may have unique risk and protective factors related their distinct environment and role-functioning characteristics (Cleveland, Mallett, White, Turrisi, & Favero, 2013). Therefore, this review was limited to prevention and intervention studies for college students. Due to the paucity of research in this area, we included all studies in which an intervention or preven- tion program that targeted drugs was implemented and at least one be- havioral outcome was measured. Articles were identified using PsychINFO and PubMed with search terms college, drug, drug use, and substance use as well as common and scientific names (where applica- ble) for each drug class (e.g. marijuana and cannabis).

Table 1 Prevalence of drug use in college students: data from three major epidemiological studies.

Monitoring the future (MTF)

Lifetime Annual Current

Any illicit drug 49.2 36.3 21.4 Any illicit drug other than marijuana 24.3 16.8 8.2 Marijuana 46.6 33.2 19.4 Synthetic marijuana – 8.5 – Inhalants 3.7 0.9 0.3 Hallucinogens 7.4 4.1 1.2

LSD 3.7 2.0 0.5 Other hallucinogens 6.9 3.4 0.8 Ecstasy (MDMA) 6.8 4.2 0.7

Cocaine 5.5 3.3 1.2 Heroin 0.6 0.1 a

Narcotics other than heroin 12.4 6.2 2.1 OxyContin – 2.4 – Vicodin – 5.8 –

Amphetamines 13.4 9.3 4.5 Methamphetamine-Ice 0.2 0.1 a

Ritalin – 2.3 – Adderall – 9.8 –

Sedatives 3.6 1.7 – Tranquilizers 7.1 4.2 1.6 Steroids 1.1 0.2 0.2

All numbers are percentages. “–” indicates data not available.

a Prevalence rate less than .05%.

1.1. Epidemiology

1.1.1. Prevalence of drug use in college students There have been several large-scale studies of the prevalence

of drug use among college students in recent years and the most comprehensive data sets are available from the Core Institute (CORE), Monitoring the Future (MTF), and National Household Survey on Drug Abuse (NHSDA). Prevalence rates listed reflect drug use from 2010 to 2011. Past year prevalence of marijuana use was 31.3–33.2% and 11.0–16.8% for illicit drug use other than mari- juana (CORE, 2010; Johnston, O'Malley, Bachman, & Schulenberg, 2012) (see Table 1 for prevalence rates from each study). The most commonly used drugs other than marijuana were Adderall (9.8%), amphetamines (9.3%), and synthetic marijuana (8.5%) (Johnston et al., 2012). Prevalence of current use (past 30 days) was estimated to be 21.4–22.0% for any drug use, 18.1–20.3% for marijuana use only, and 5.5–8.2% for drug use other than marijuana (CORE, 2010; Johnston et al., 2012; SAMHSA, 2010). For current use, the most commonly used drugs after marijuana were misused prescriptions drugs (6.3%) (SAMHSA, 2010) and amphetamines (2.7–4.5%) (CORE, 2010; Johnston et al., 2012). Among current marijuana users, 7.2% of students reported using marijuana 3 times a week or more frequently, and 4.7% reported using marijuana at least 20 days every month (CORE, 2010; Johnston et al., 2012). Overall, although current prevalence rates for each individual drug type other than marijuana are low, about 1 in 5 college students use drugs (including marijuana) each month, and approximately 5% of students report near daily use.

CORE NHSDA

Lifetime Annual Current Lifetime Annual Current

– – – – – 22.0 – 11.0 5.5 – – 6.4 44.1 31.3 18.1 – – 20.3 – – –

3.1 1.0 0.4 – – 0.6 7.9 4.1 1.2 – – 1.9 – – – – – 0.3 – – – – – –

– – – – – 1.1 7.7 3.7 1.3 – – 1.6 – – – – – 0.2 – – – – – –

– – – – – 0.4 – – – – – –

10.1 5.0 2.7 – – 1.6 – – – – – 0.1 – – – – – –

6.7 3.4 1.5 – – 0.1 – – – – – 1.4 1.0 0.5 .4 – – –

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1.1.2. Drug-related consequences in college students Drug use puts college students at risk for experiencing a range of

adverse health, behavioral, and social consequences. Although there is a lack of representative data on drug use morbidity and mortality among college students, based on CDC estimates approximately 1000 college students die from drug overdoses each year (Hingson & White, 2010). College drug users are also at risk for developing a drug use disorder, characterized by the development of physiological and psychological tolerance, use of the drug even in the presence of adverse effects, and forgoing social, occupational, or recreational activities because of drug use (Budney, 2007). A recent study of college freshman found that 9.4% of students met criteria for cannabis use disorder, and that this number jumped to 24.6% among past-year marijuana users (Caldeira, Arria, O'Grady, Vincent, & Wish, 2008).

Even in absence of a diagnosable disorder, the use of marijuana is associated with a wide range of consequences including legal and health problems (Presley, Meilman, & Cashin, 1996). Students who reported using marijuana and alcohol perform more poorly on tests, are more likely to miss class, and self-report more memory problems than students who reported only using alcohol (Rhodes, Peters, Perrino, & Bryant, 2008; Shillington & Clapp, 2001). Among students who reported using marijuana 5 or more times in the past year, 40.1% reported experiencing concentration problems and 13.9% reported missing class due to marijuana use (Caldeira et al., 2008). Marijuana users also receive lower grades in college and are more likely to drop out (Arria et al., 2013; Bell, Wechsler, & Johnston, 1997; Buckner, Ecker, & Cohen, 2010). A longitudinal study that followed students over four years of college found that drug use over- all was predictive of college attrition even after controlling for paren- tal level of education (Martinez, Sher, Krull, & Wood, 2009). Researchers have found that nonmedical users of prescription stimu- lants and analgesics skipped more classes (21%) than non-users (9%) (Arria, Caldeira, O'Grady, Vincent, Fitzelle, et al., 2008; Arria, Caldeira, O'Grady, Vincent, Johnson, et al., 2008). Furthermore, after controlling for high school GPA, nonmedical use of prescription drugs was associ- ated with lower GPAs by the end of the freshman year of college and skipping classes partially mediated this relationship.

Drug use has also been linked to a number of risky behaviors. Caldeira et al. (2008) found of the students who reported using marijua- na 5 or more times in the past year, 24.3% reported putting themselves at risk for physical injury high. In one study, 47% of current marijuana users reporting driving after smoking marijuana (McCarthy, Lynch, & Pederson, 2007), and in another 18.6% reported driving while high (Caldeira et al., 2008). Drug use has also been linked to risky sexual behavior (Simons, Maisto, & Wray, 2010), including not using condoms (Caldeira et al., 2009), and STI diagnosis (Vivancos, Abubakar, & Hunter, 2008). Thus, although most college student drug users use drugs rela- tively infrequently, they are nevertheless at significant risk for a variety of adverse social, legal, academic, and health-related consequences.

2. Psychosocial factors related to college student drug use

2.1. Demographic and lifestyle factors

College men have higher annual prevalence rates of marijuana and most other drugs than women (Johnston et al., 2012; McCabe, Cranford, Boyd, & Teter, 2007; McCabe, Morales, et al., 2007); however, findings are less consistent for nonmedical use of prescription drugs (Matzger & Weisner, 2007; McCabe, 2005; McCabe, Teter, & Boyd, 2006). Caucasian students tend to have higher rates of drug use than eth- nic minority students (McCabe, 2005; McCabe, Knight, Teter, & Wechsler, 2005; Mohler-Kuo, Lee, & Wechsler, 2003), although more re- search is needed to evaluate this and one study found that Latino stu- dents reported more drug use than other students (McCabe, Morales, et al., 2007). Students who identify as gay, lesbian, or bisexual (Boyd, McCabe, & d'Arcy, 2003; Reed, Prado, Matsumoto, & Amaro, 2010) or

who belong to a fraternity or sorority (McCabe, Knight et al., 2005; McCabe, Schulenberg, et al., 2005; McCabe, Teter, & Boyd, 2005) are also more likely to use drugs. Identifying as religious (Hammermeister, Flint, Havens, & Peterson, 2001; Helm, Boward, McBride, & Del Rio, 2002) and participating in college athletics have been shown to be pro- tective factors against drug use (Ford, 2007; Yusko, Buckman, White, & Pandina, 2008).

2.2. Personality and psychiatric comorbidity

Drug use has been studied in relation to a number of different per- sonality traits and models. Neuroticism, the tendency to experience strong negative emotions in reaction to stress; psychoticism, a person- ality characterized by interpersonal hostility; and, and sensation- seeking have been shown to be significantly associated with meeting criteria for drug dependence disorder, but interestingly these traits do not all predict drug use uniquely after controlling for alcohol and tobac- co use disorders (Grekin, Sher, & Wood, 2006; Sher, Bartholow, & Wood, 2000). Sensation-seeking, however, has been found to predict drug use disorders seven years later even after controlling for alcohol-use disor- ders (Sher et al., 2000). Overall, both cross-sectional and longitudinal studies implicate sensation-seeking as a strong predictor of drug use in college students (Arria, Caldeira, O'Grady, Vincent, Fitzelle, et al., 2008; Arria, Caldeira, O'Grady, Vincent, Johnson, et al., 2008; Low & Gendaszek, 2002). Perceived harmfulness of the drug may moderate this relationship (for stimulant and marijuana use but not for analgesic use) with those who perceived the drug as being more harmful being less likely to report drug use despite high levels of sensation seeking (Arria, Caldeira, O'Grady, Vincent, Fitzelle, et al., 2008; Arria, Caldeira, O'Grady, Vincent, Johnson, et al., 2008).

Impulsivity refers to a general difficulty in inhibiting responses and a tendency to overvalue immediate relative to delayed rewards (Madden & Bickel, 2009). Although impulsivity has been consistently associated with increased risk for drug use in general adult samples (de Wit, 2009; Reynolds, 2006), results with college samples have been less consistent. One possible explanation for these conflicting results is that impulsivity may be a risk factor for the use of certain types of drugs, but not others. Significant relationships were most often found in studies that examined drug use as a whole, while non-significant re- sults in some studies that examined marijuana use (Simons & Carey, 2002, 2006; Simons, Neal, & Gaher, 2006; Stanford, Greve, Boudreaux, & Mathias, 1996). Delay discounting, a behavioral economic measure of impulsivity, has been shown to be associated with earlier age of first marijuana use and a greater number of illicit drugs used in a college sample (Kollins, 2003). It is possible that impulsivity may also be related to more general deficits in executive functioning and decision making (Hammers & Suhr, 2010), and that impulsivity may be both a cause and a consequence of drug use (Perry & Carroll, 2008). One study found that polysubstance users did worse on various tests of executive functioning than did controls who were matched on personality charac- teristics (including impulsivity) which suggests that these individuals have decreased decision-making and executive-functioning capacity (Hammers & Suhr, 2010). Finally, impulsive individuals may be less likely to attend college, which may restrict range on this variable. Research regarding the relationship between ADHD, a disorder cha- racterized by executive dysfunction and impulsivity, and drug use has been equivocal; one study found that students with ADHD are more likely to use marijuana and other illicit drugs (Rooney, Chronis- Tuscano, & Yoon, 2011) while other studies suggest that ADHD is associated with elevated risk for marijuana use (Baker, Prevatt, & Proctor, 2012). One study demonstrated that reporting experiencing a greater number of conduct disorder symptoms prior to age 15 was associated with more drug dependence symptoms in college (Grekin et al., 2006).

Studies have shown marijuana use to be related to negative affect, personality disorders, and psychoticism, but unrelated to social

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anxiety (Buckner, Bonn-Miller, Zvolensky, & Schmidt, 2007; Buckner et al., 2010). Students diagnosed with a psychiatric disorder are at greater risk for more consequences related to drug use (Dunn, Larimer, & Neighbors, 2002; Goldstein, Flett, Wekerle, & Wall, 2009; Rooney et al., 2011). One study found that 17% of the variance in drug use could be accounted for by scores on the Beck Depression In- ventory (Helm et al., 2002). Higher levels of negative affect have been linked to prescription stimulant misuse (both any use and frequency of use), and for experiencing more problems related to drug use gen- erally (Ford & Schroeder, 2009; James & Taylor, 2007; Teter, Falone, Cranford, Boyd, & McCabe, 2010). There is some evidence that nega- tive affect is a stronger risk factor for substance use in college men compared to women (Helm et al., 2002), and in Caucasian compared to African-American students (Mounts, 2004). Students who report experiencing panic attacks are more likely to have reported using sedatives, but not any other type of drugs (Deacon & Valentiner, 2000) and students who engage in vomiting, fasting, and laxative use are more likely to use stimulants than other drugs presumably to aid in weight loss in connection with eating disorders (Dunn, Neighbors, Fossos, & Larimer, 2009).

Affect dysregulation refers to difficulties in regulating affect and the resulting behavioral consequences that lead to impaired function- ing (Cole, Michel, & Teti, 1994). Affect lability, defined as rapid shifts in emotional expression, and impulsivity are key components of affect dysregulation, which has been prospectively associated with increases in drug-related problems among college students (Simons & Carey, 2002, 2006). This is consistent with previous research that supports individual temperament characteristics as being more pre- dictive of problematic use patterns than social or environmental factors (Glantz, 1999). Difficulties with negative emotions are often related to the experience of traumatic or negative life events (e.g., the death of a loved one, or abuse) which is another established risk factor for drug among college students (Taylor, 2006).

2.3. Social, cognitive, peer, and family influence on drug use

2.3.1. Social influences A great deal of research has demonstrated that peer attitudes and

behaviors influence college substance use (Neighbors, Lee, Lewis, Fossos, & Larimer, 2007). Students who reported that they had friends who used drugs and alcohol regularly reported higher levels of drug use related problems themselves (Taylor, 2006), and the influence of peer use on drug use has been replicated with Asian American (Liu & Iwamoto, 2007) and African American students as well (Pugh & Bry, 2007). Although peers are a key influence on substance use behavior of college students, parent and sibling drug use can also increase risk for drug use (Brook, Brook, & Whiteman, 1999).

2.3.2. Drug use norms, motives and expectancies Social Norms Theory is based on the idea that individuals

misperceive others' actions and perceptions related to a behavior (e.g., drug or alcohol use) and that these misperceptions in turn influ- ence the individual's behavior (Martens et al., 2006). Social norms have commonly been divided into two categories: descriptive norms (e.g., perceived frequency of drug use by one's peers) and injunctive norms (e.g., perceived approval of drug use by one's peers) (Borsari & Carey, 2003). Research suggests that norms account for up to 42% of the variance in marijuana use even after controlling for sociodemographic variables (Lewis & Clemens, 2008). Perceived frequency of use for close friends (of either gender) is the normative reference group that is most predictive of personal use. Students who perceive that their friends use marijuana more frequently and approve of marijuana use also tend to use more marijuana than stu- dents with fewer friends who used marijuana and have low levels of perceived friend approval of marijuana (Neighbors, Geisner, & Lee, 2008; Simons et al., 2006). Although these cross-sectional studies

suggest a relationship between normative beliefs and use, the direc- tion of the relationship is unclear, and longitudinal studies are neces- sary to establish causality (Bustamante et al., 2009).

College students report a number of motives for using drugs and studies have shown that student motives for using can be risk factors for greater levels of drug use and problems. A qualitative study sug- gested that the most common motives for nonmedical use of prescrip- tion drugs were getting high, partying, experimenting, facilitating social interactions, and helping to structure free time (Quintero, 2009); however, motives tend to differ for different types of nonmedical prescription drug use. The primary motives of nonmedical prescription pain medication tend to be to get high or to alleviate pain (McCabe, Teter, et al., 2005), and those who used for motives other than to relieve pain (e.g., to get high) had more problems related to their drug use (McCabe, Cranford, et al., 2007). The most common motives for nonmedical use of prescription stimulants have been shown to be to help with concentration (58%), to increase and sustain alertness (43%), and to “get high” (43%) (Teter, McCabe, Cranford, Boyd, & Guthrie, 2005). Nonmedical use of ADHD medication is increasingly common and it appears that the majority of students use stimulants in times of increased academic stress, but that some (approximately 15% in one study) also use these drugs to get high or to enhance partying (Arria, Caldeira, O'Grady, Vincent, Fitzelle, et al., 2008; Arria, Caldeira, O'Grady, Vincent, Johnson, et al., 2008; DeSantis, Webb, & Noar, 2008).

Simons, Correia, Carey, and Borsari (1998) found that expansion motives (using drugs to increase experiential awareness), enhance- ment motives (using drugs to enjoy the feeling of being high), and coping motives were associated with marijuana use, with a stronger relationship between coping, and use for women. Social and confor- mity motives were not significant predictors of marijuana use, but social motives were related to a greater number of problems related to use (Simons et al., 1998). It appears that enhancement motives are important in marijuana use, but more research is necessary to examine associations between other motives and marijuana use and related problems.

Expectations regarding the positive and negative effects of a sub- stance are related to levels of substance use and related problems (Gaher & Simons, 2007), with positive expectancies predicting more use. Studies have shown that the most common expectancies associated with marijuana use include social facilitation, tension reduction/affect regulation, cognitive impairment and perceptual enhancement (Simons & Carey, 2006; Simons, Gaher, Correia, & Bush, 2005). Studies have also shown that marijuana users tend to associate marijuana with more positive outcomes and fewer nega- tive outcomes (Kilmer, Hunt, Lee, & Neighbors, 2007), and to rate the consequences as being less negative (Gaher & Simons, 2007) compared to nonusers. Positive social expectancies are related to increased marijuana use and men report higher social expectancies for marijuana than do women (Neighbors et al., 2008). Research on expectancies and consequences is more equivocal. Students who perceived driving after using marijuana as less dangerous and reported greater perceived peer acceptance of driving after use were more likely to report this behavior (McCarthy et al., 2007). Conversely, one study reported that frequency of use was not related to perceived likelihood of consequences (Kilmer et al., 2007), and another study reported that although greater expected likelihood of negative outcomes were associated with less use, the expected degree of negativity of the outcomes was not related to level of use (Gaher & Simons, 2007). Although research on expectancies and drugs other than marijuana is sparse, one study found that use of prescription stimulants was related to greater expectancies about the drug's potential positive effects including staying awake, study- ing better, and losing weight (Carroll, McLaughlin, & Blake, 2006). As is the case with most studies of risk factors included in this review, the cross-sectional nature doesn't allow for causal conclusions.

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Some of the risk factors identified above may be helpful to consid- er when planning prevention research and outreach efforts. Students who identify as gay, lesbian, or bisexual (Boyd et al., 2003; Reed et al., 2010), fraternity or sorority members (McCabe, Knight, et al., 2005; McCabe, Schulenberg, et al., 2005; McCabe, Teter, et al., 2005), and students who attend university counseling or mental health clinics should be prioritized in university drug prevention efforts given their elevated levels of risk for drug use. In general, there is evidence that drug use may exacerbate psychiatric symptoms and that individ- uals with more severe symptoms may use more often, perhaps as a means of coping with negative affect, and intervention approaches should include a focus on developing alternative methods for coping with stress (Geisner, Neighbors, Lee, & Larimer, 2007; Murphy, Dennhardt, et al., 2012; Murphy, Skidmore, et al., 2012). Intervention approaches that directly target deficits in impulsivity or executive functioning may also be useful (Bickel, Yi, Landes, Hill, & Baxter, 2011). Normative beliefs have also been implicated as a risk factor and interventions that attempt to reduce marijuana use by providing corrective feedback on the actual prevalence of use (normative be- liefs) may be effective and warrant further research (Lee, Neighbors, Kilmer, & Larimer, 2010). Motives and expectancies for drug use are also part of established theories of substance abuse and intervention approaches are unlikely to be successful if they do not address com- mon motives such as stress reduction, social facilitation, and cogni- tive/mood enhancement. For example, students who misuse prescription drugs to cope with pain or to study more effectively may benefit from intervention approaches that help them to develop alternative means of addressing these goals. The next section will re- view prevention and intervention studies for college drug use, pointing out the relevant risk factor targeted by each intervention if applicable. Finally, recommendations for developing further interven- tions will be given.

3. Drug use prevention studies for college students

Colleges and universities are especially critical settings for preven- tion and early intervention given that it is the gateway to adulthood for nearly half of the US population and the period during which most young adults initiate or increase drug use (Johnston et al., 2012). Colleges are also well equipped to implement fairly large scale prevention and brief intervention programs. Many now do so for alcohol abuse but very few colleges have systematic approaches to preventing or reducing drug use. The following sections review studies that attempted to reduce or prevent drug use in general student populations (prevention studies) and studies that attempted to reduce drug use in samples of students who report using drugs (intervention studies). (See Table 2 for characteristics of studies included in this section).

3.1. Uncontrolled studies

The Fund for the Improvement of Postsecondary Education for col- legiate alcohol and other drug prevention efforts awarded funds for 188 institutions to create drug prevention programs. Rather than dic- tate a protocol, these funds allowed institutions to create a program that they felt fit their needs. The most common elements used by pro- grams were the distribution of flyers and brochures, educational pre- sentations, alcohol-free activities, faculty and staff training, and peer education programs. The specific intervention elements were not specified. CORE survey data from roughly 41,000 students was com- pared pre and post program. Surprisingly, results demonstrated that prevalence rates (adjusted for 2-year trends using the Monitoring the Future data) showed an increase in marijuana and cocaine use fol- lowing the implementation of these programs (Licciardone, 2003). It is difficult to interpret these results in the absence of a control group or information regarding the program content, but these findings

likely highlight the ineffectiveness of primarily informational ap- proaches to reducing drug use and the importance of developing and disseminating evidence based approaches to drug use prevention (Larimer, Kilmer, & Lee, 2005).

3.2. Controlled studies

The Campus wide Alcohol and Drug Abuse Prevention Program was implemented from 1988 to 1989 at the University of New Mexico (n = 567) (Miller, Toscova, Miller, & Sanchez, 2000) and compared to a control campus (n = 457). The program aimed to increase alco- hol and drug use risk perception by disseminating information about the harms associated with drug use throughout the university (e.g., pamphlets, newspaper stories, lectures, computerized information programs, trained peer educators). When compared to the control campus, students at the University of New Mexico campus had signif- icantly higher levels of perceived risk of substances and had reduced levels of marijuana (and alcohol) use at the end of the 1.5 year inter- vention period. As previously described, research suggests that in- creasing perceived risk may reduce the likelihood of using drugs for those high in sensation-seeking (Arria, Caldeira, O'Grady, Vincent, Fitzelle, et al., 2008; Arria, Caldeira, O'Grady, Vincent, Johnson, et al., 2008). Although this study did not measure sensation-seeking or whether perceived risk mediated outcomes, this intervention may be promising for college drug users with elevated sensation seeking. Results from alcohol prevention research studies (reviewed by Cronce & Larimer, 2011) would suggest that the individualized com- puter and peer-administered interventions may have been the most effective treatment elements. Reductions in marijuana may also be a secondary effect of alcohol interventions (Grossbard et al., 2010; Magill, Barnett, Apodaca, Rohsenow, & Monti, 2009).

Marcello, Danish, and Stolberg (1989) developed and tested a sub- stance use prevention program for college athletes. Students were assigned to an intervention group or to a wait-list control condition. The group based intervention was delivered in three 2-hour compo- nents and included: (a) Education, (b) Skill Training for Prevention, and (c) Skills to Deal with Peer Pressure. The education component provided the students with general education about various drugs, definitions of use, abuse, and addiction, information about etiological factors involved in drug use and the types of treatments available. The Skills Training for Prevention aimed to teach the students decision- making and coping skills to use to avoid high-risk situations in which they might use substances. The Skills to Deal with Peer Pres- sure component provided the students with the rationale that increased assertiveness and ability to resist peer pressure would de- crease the likelihood they would use drugs. The goal of this program was to help students make responsible decisions about drug use rath- er than to focus solely on abstinence. Although this intervention included many components that have demonstrated efficacy in alco- hol prevention studies (Cronce & Larimer, 2011), there were no sig- nificant differences in drug use between the intervention and control group at the 2-month follow-up. Possible mechanisms of change consis- tent with the risk factors identified above (coping, anxiety, attitudes, adaptive skills, etc.) were also measured pre-intervention, immediately post-intervention, and at 2-months post-intervention. There was a sig- nificant treatment effect for trait anxiety with the intervention group displaying significantly less anxiety immediately post-intervention; however, this reduction in anxiety did not lead to less drug use at 2-month follow-up. Because only 58 of the 110 students recruited into the study completed the follow-up the lack of drug use treatment ef- fects may be related to poor power and/or attrition. The authors of this study also noted that they may have been too ambitious in trying to accomplish the goals of the intervention in 6 hours. It is also possible that the focus on didactic information, rather than motivational en- hancement and skill training, may have undermined student motiva- tion and engagement.

Table 2 Prevention and intervention studies for college student drug use.

Study Sample characteristics (% of eligible sample recruited if available)

Assessments (% retained from recruited sample)

Intervention conditions Outcomes

Amaro et al., (2010)

(49%) Binge drinking or drug using students recruited through University Health Center

449, Post-intervention and 6-month follow-up

1. 2-session (45–60 min each) BASICS intervention with alcohol self-monitoring and personalized feedback

Reduced marijuana and cocaine use for heavy users

Elliot and Carey (2012)

245 college students who reported no past-month marijuana use

241, (98.4%) 1-month follow-up 1. e-TOKE (computerized) 2. Assessment-only

Lower descriptive norms, and fewer believed friends disapproved of abstaining in e-TOKE group. No difference in marijuana initiation.

Fischer et al. (2013)

134 (69%)Canadian college students who had used marijuana for at least 1 year and 12 of past 30 days

113, 3-month follow-up (84%) 1. Marijuana in-person BMI 2. Marijuana written BMI 3. Health in-person BMI 4. Health written BMI

Reduced marijuana use across conditions. Reduction in specific risky MJ behaviors in the treatment conditions (1 and 2).

Grossbard et al. (2010)

1275 (32%) incoming college students who had participant in high school athletics

1096, 10 month follow-up (86%) 1. BASICS only 2. Parent only 3. BASICS + parent 4. Control

Lower past-month marijuana use in BASICS + parent condition.

Lee et al. (2010)

341 incoming college students who used marijuana in the past 3 months (92.16%)

324, 3-month follow-up (95.01%) 322 6-month follow-up (94.42%)

1. Web-based intervention with personalized feedback targeting marijuana use 2. Assessment-only control

Students with a positive family history reduced drug use at both follow-ups Students with higher motivation to change reduced at 3 months

Licciardone (2003) 41,567 students who took the CORE survey 39,197 student CORE survey post-program (ecologic study)

1. Drug prevention programs at 188 colleges created to meet the needs of that institution

Increase in cocaine and marijuana use in prevention group (no control group)

Looby et al. (in press)

106 “at-risk” college students with no previous Rx stimulant use.

96, 6-month follow-up (91%) 1. Expectancy Challenge Intervention 2. Assessment only control

Modified expectancies in intervention group, but no differences in Rx stimulant use at 6-months

Marcello et al. (1989)

110 college athletes (49%) 58, post-program assessment (64%) 57, 2-month follow-up assessment (63%)

1. Education, skill training, skills to deal with peer pressure (2 h each) 2. Delayed-intervention/control

No differences in drug outcomes between groups at 2 months

McCambridge and Strang (2004)

200 drug-using students enrolled in vocational colleges in London

179, 3-month follow-up (89.5%) 1. 1-hour motivational interviewing (MI) 2. Education as usual

Reductions in illicit drug use at 3 months in MI group

Miller et al. (2000)

1024 students who responded to a mailed survey (41.2%)

865, 1.5 year follow-up (quasi-experimental design)

1. Campus-wide alcohol and drug abuse prevention program (most components focused on alcohol) 2. Control campus (no program)

Lower levels of marijuana use than control campus

Williams et al. (1983)

24 Freshman and Junior students at a junior college

24, 3-month follow-up (100%) 1. Assertiveness training 2. 4 hour group discussion on assertiveness, drug use (control)

Lower cocaine and amphetamine use in intervention group

White et al. (2006)

235 mandated students 222, 3-month follow-up (94%) 1. MI with personalized feedback 2. Written feedback only

Lower prevalence of MJ use and fewer MJ problems across conditions

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2613A.A. Dennhardt, J.G. Murphy / Addictive Behaviors 38 (2013) 2607–2618

A computerized, norm-correcting intervention program has also been examined to prevent the initiation of marijuana use (Elliot & Carey, 2012). The intervention program, Marijuana eCHECKUP TO GO (e-TOKE), consists of an assessment phase and provides feedback about the students' marijuana use patterns, risk factors, and per- ceived versus actual marijuana use norms. For this study, 245 college students who were current abstainers (past month) from marijuana were randomly assigned to e-TOKE or an assessment-only condition. One month later, all students reported on their marijuana use, de- scriptive norms, and injunctive norms. Although students in the e-TOKE program estimated lower descriptive norms and that fewer friends disapproved of their choice to abstain, rates of initiation did not differ between the two conditions. Thus, although e-TOKE ap- pears to be helpful in correcting perceived norms about marijuana use (an established risk factor), this correction does not translate into lower marijuana initiation rates one month later. It is possible that a longer follow-up period would be necessary for differences to be seen. The alcohol intervention counterpart, e-CHUG, has been somewhat successful in helping students to reduce their alcohol consumption (Alfonso, Hall, & Dunn, 2012, but see also Murphy, Dennhardt, Skidmore, Martens, & McDevitt-Murphy, 2010), but more research is necessary to examine the efficacy of e-TOKE and the role of norm correction in prevention and intervention efforts for marijuana use.

Expectancy challenges (an experiential intervention designed to modify positive expectancies about a substance) have been widely used in the college drinking literature, but until recently have not been applied to drug use (Lau-Barraco & Dunn, 2008). Looby, Young, and Earleywine (in press) designed an expectancy challenge intervention to prevent nonmedical prescription stimulant use. Participants were 96 students who were at-risk for prescription stim- ulant use based on having a low grade point average, Greek involve- ment, binge drinking, and cannabis use, but reported no previous nonmedical use of prescription stimulants. They were randomized to an experimental condition in which they orally ingested a placebo stimulant or to a control group that did not receive medication. Researchers found that participants in the experimental group reported feeling significantly more high and stimulated compared to the control subjects. These students were then told that they had ingested a placebo to illustrate the role of their expectancies for the drug. This intervention also included a broad didactic lecture and dis- cussion on expectancy effects and the potential negative conse- quences of nonmedical use of stimulants. The expectancy challenge was successful in modifying expectancies, but the intervention group and a control group showed comparable rates of nonmedical prescription use at 6-month follow-up. Across conditions, negative expectancies were significant predictors of reduced odds of future use. These results provide evidence that expectancies play a role in drug effects but do not support the use of expectancy challenge ap- proaches as a stand along intervention for drug use.

4. Drug use intervention studies for college students

4.1. Uncontrolled studies

Brief motivational interventions (BMIs) with personalized feed- back have been shown to be efficacious in reducing alcohol use in college students (Cronce & Larimer, 2011), but very few studies have examined drug use outcomes. One study examined the efficacy of a BMI with students recruited through the University Health and Counseling Services (Amaro et al., 2010). Participants were either re- ferred by the campus health center providers (33%) or self-referred (67%). To be eligible for the study, once referred participants had to endorse two of six statements on an alcohol and drug use screening measure (CRAFFT; Knight, Sherritt, Shrier, Harris, & Chang, 2002). Eligible participants received a 2-session (45–60 min each) BASICS

intervention in which the feedback components (e.g., data on the student's alcohol consumption, perceptions of other students' drink- ing compared to actual usage data, blood alcohol content, beliefs about alcohol, consequences, and risk factors) were delivered using a motivational interviewing style. Analyses were conducted on the 449 students who received the intervention and completed a follow-up assessment 6 months later (56% reported drug use at base- line). Results indicated that high frequency drug users (10 times or more in the past 6 months) reported reduced (self-reported) mari- juana and cocaine use 6 months after the intervention. The students experiencing the most drug-related negative consequences (top tertile) reported reduced consequences at follow-up. Although the absence of a control group precludes causal inference, this study sug- gests that a BMI targeting alcohol may be effective for helping college students reduce their drug use. Although this study did not target drug use, a number of risk factors common to alcohol and drug use were addressed (normative beliefs, motives and expectancies). However, these risk factors were addressed in relation to alcohol (e.g., corrective norms on alcohol use) so their role in drug use out- comes is unclear.

4.2. Controlled studies

One study examined an assertiveness training intervention in a sample of 24 students who reported low assertiveness and drug use (Williams, Hadden, & Marcavage, 1983). Students in the intervention condition (n = 12) received instruction on how to respond assertive- ly and then were given feedback on responses they gave in a variety of hypothetical situations. Participants assigned to the control group participated in a four-hour small-group discussion on assertiveness, peer pressure, and drug use but did not receive behavioral skills train- ing or rehearsal. Students in the intervention condition reported more assertiveness, more incidents of refusing drug use when pressured by a peer, and lower use of cocaine, marijuana, and stimu- lants at 3-months post intervention. These results are consistent with the promising results obtained with similar approaches to alcohol prevention (Kivlahan, Marlatt, Fromme, Coppel, & Williams, 1990), but inconsistent with the findings of Marcello et al. (1989) and sug- gest that skills based approaches to reducing drug use may be espe- cially effective for less assertive students. These results must be interpreted cautiously due to the extremely small sample size. This intervention targets assertiveness, and although poor assertiveness has not been identified as a direct risk factor for drug use it may con- fer indirect risk via its association mood or anxiety disorders. This study, however, did not examine participants' mental health and is therefore unable to determine if the intervention is more efficacious for students with a psychiatric disorder.

Several controlled trials have investigated individual single ses- sion drug use interventions for college students that include motiva- tional interviewing and/or personalized feedback components. McCambridge and Strang (2004) examined the efficacy of a BMI to re- duce drug use among students (ages 16–20) at 10 vocational or junior colleges in London. Participants endorsed weekly cannabis or stimu- lant drug use within the past 3 months and were randomized to ei- ther a BMI intervention or an assessment-only condition. The BMI included a decisional balance exercise, a discussion of the actual and potential consequences of the student's drug use, as well as the rela- tion between drug use and the student's values and goals. It did not include personalized feedback, which is a key feature of effective brief alcohol interventions (Walters, Vader, Harris, Field, & Jouriles, 2009). At the three month follow-up, students who received the BMI reduced their use of marijuana and other non-stimulant illicit drugs more so than students in the assessment-only group. The re- duction in cannabis in the intervention condition was particularly compelling as the mean frequency of weekly use decreasing from 15.7 times per week to 5.4 times per week. There was a 27% increase

2614 A.A. Dennhardt, J.G. Murphy / Addictive Behaviors 38 (2013) 2607–2618

in frequency within the control group. Thus, individual counselor delivered motivational interviews may show promise for reducing marijuana use among college students. Another study examined the efficacy of a BMI with a clinician versus written material only in a sample of heavy marijuana users at a Canadian university (Fischer et al., 2013). The interventions consisted of short, fact-based and non-judgmental information on cannabis-related health risks, strate- gies to reduce risk, and motivational components. The 20–30 minute clinician-delivered intervention was presented in an interactive and nonjudgmental style. Student could be randomized to one of these two intervention conditions or one of two control conditions which delivered general health information by a clinician (in the same for- mat described above) or written form only. At three months post- intervention, there was a decrease in the mean number of marijuana use days across the four conditions and a trend-level effect for greater reductions in marijuana days for the two intervention conditions. Students who received the clinician-delivered intervention reduced deep inhalation/breathholding (a risk factor for acute or long-term health problems) and those who received the written-only interven- tion reduced driving after cannabis use compared with controls (Fischer et al., 2013). This study suggests that brief interventions may be helpful in reducing some of the risky marijuana use behaviors, but it is unclear if a clinician-delivered or written-only format is more efficacious. More work is needed to identify mechanism of brief motivational interventions for drug use. Motivational interviewing for alcohol use appears to work by altering normative beliefs about typical rates of alcohol consumption (Walters et al., 2009).

A study with US college students provided additional support for the efficacy of BMIs for college drug use. White et al. (2006) examined a BMI with feedback (n = 118) and a feedback-only intervention (n = 104) with mandated students who received an alcohol (88.6%) or marijuana violation (11%). Both interventions provided feedback (including corrective normative feedback) on students' sub- stance use (similar to Amaro et al., 2010 except included feedback on illicit drug use in addition to alcohol), but in the BMI condition the feedback was presented in the context of a counseling session. Results were not examined separately for students who were cited for mari- juana use. Students reported significant reductions in marijuana use and problems at the 3-month follow-up with no significant differ- ences across conditions. This suggests that feedback interventions that also include a focus on alcohol may be effective for reducing mar- ijuana problems and promoting abstinence, even in the absence of an individual counseling session. As noted earlier it is possible that re- ductions in alcohol use may have facilitated reductions in marijuana use (Grossbard et al., 2010).

Despite these promising results, two other studies have failed to find effects for computerized feedback targeting marijuana use among college students. Lee et al. (2010) evaluated a brief, web- based intervention for at-risk marijuana users transitioning to college (n = 98) compared to an assessment only control condition (n = 88). This study is noteworthy in that it identified participants on the basis of drug use rather targeting drug use as a secondary outcome in at-risk drinkers. Students viewed computer-delivered personalized feedback including information about their marijuana use, compari- son of perceived norms to actual norms, perceived pros and cons of use, and self-reported consequences of marijuana use. They also re- ceived a list of behavioral strategies to reduce their marijuana use and problems. In contrast to the overall reduction associated with personalized drug use feedback found by White et al. (2006), Lee et al. found no overall effect on self-reported marijuana use at three and six month follow-ups, but did find that family history of drug problems and motivation to change moderated outcomes. Those who received the intervention and were higher in motivation to change significantly reduced their drug use at 3-month follow-up. Individuals in the intervention condition with a positive family histo- ry of drug problems showed a marginally significant reduction in

marijuana use at 3-months, and a significant reduction at the 6-month follow-up (Lee et al., 2010). Thus, brief computerized feed- back on marijuana use may be helpful for students with some motiva- tion to change or a family history of drug use. These studies suggest that targeting normative beliefs about drug use may be an important aspect of drug use interventions, but additional follow-up studies are needed to confirm this relationship.

Parent based interventions have shown promise as an adjunct to brief alcohol interventions with college students (Turrisi et al., 2009; Wood, Capone, Laforge, Erickson, & Brand, 2007) and may also curtail drug use among college students. Grossbard et al. (2010) compared an in-person BMI with personalized feedback (n = 277), a parent-based intervention (n = 316), a combined BMI and parent intervention (n = 342), and a control condition (n = 340), for incoming first year students who had participated in high school athletics, but did not necessarily report drinking or drug use. The intervention focused primarily on reducing current or preventing future drinking and did not include any drug use treatment elements or target any specific risk factors. Students who were assigned to the BMI or the control condition showed significantly higher past-month marijuana use than the combined BMI and parent intervention (Grossbard et al., 2010), which suggests a possible prevention effect for the combined condition. The intervention did not impact other illicit drug use, but did reduce risky drinking (Turrisi et al., 2009). Future research should investigate parent based interventions that include a drug use component and with samples of students selected on the basis of drug use.

5. Summary

The research thus far suggests that campus wide educational ap- proaches are unlikely to be successful (Licciardone, 2003), but that brief counselor delivered motivational interventions may be an effec- tive method for helping college students to reduce their drug use. Although several preliminary studies suggest that brief motivational and skills-based interventions may be effective in helping students reduce their marijuana use, little is known about interventions that identify students on the basis of drug use (versus identifying alcohol users who also use drugs), and in particular drug use other than mar- ijuana. Additionally, although there are conflicting results, two well controlled studies suggest that feedback-only BMIs for drug use are not generally effective (Elliot & Carey, 2012; Lee et al., 2010), though they may be effective for students who are motivated to change or have a positive family history of drug use (Lee et al., 2010), when they include a focus on alcohol and drug use (White et al., 2006), or in helping to reduce risky behavior associated with drug use (Fischer et al., 2013). The combination of individual BMI and parent-based approaches is promising and merits further study.

6. Future directions in college drug use prevention and intervention

Almost 50% of college students report lifetime use of drugs and many experience negative consequences associated with their drug use such as lower academic performance, risky behaviors such as unprotected sex or driving after using drugs, and legal and health prob- lems. Men, European Americans or Latinos, students involved in Greek organizations, GLB students, and non-religious students are more likely to use drugs. Personality factors such as high levels of neuroticism, psychoticism, impulsivity, and novelty or sensation-seeking increase risk for drug use, as does negative emotionality, emotional dys- regulation and the presence of personality disorders. Drug use has also been linked to socio-environmental factors such as overestimating normative levels of drug use and experiencing negative life events. College student drug users are also at risk for developing a drug use disorder or accidental overdose, particularly when they engage in

2615A.A. Dennhardt, J.G. Murphy / Addictive Behaviors 38 (2013) 2607–2618

mixing sedatives or analgesics with alcohol (Budney, 2007; Caldeira et al., 2008; Johnston et al., 2012; Presley et al., 1996).

Longitudinal studies are essential to better understand the trajec- tory of drug use during the college years and beyond. Although the proportion of students who drink and use drugs increases during each year of college (Arria, Caldeira, O'Grady, Vincent, Fitzelle, et al., 2008; Arria, Caldeira, O'Grady, Vincent, Johnson, et al., 2008), many students who are heavy drinkers “mature out” of this pattern (Demb & Campbell, 2009), but there is little research examining this process in college student drug users. Current research demonstrates that there are different trajectories of marijuana use, but does not provide information regarding possible individual-level risk factors for more severe trajectories in use (Caldeira, O'Grady, Vincent, & Arria, 2012).

Although factors that predict escalating use are unknown, infor- mation about trajectories of drug use that predict long-term harm can be helpful in informing prevention and intervention. Preliminary evidence suggests that differing trajectories of marijuana use predicts long-term mental and physical health outcomes (Caldeira et al., 2012). Despite no differences in health indicators during the first year of college, students who used marijuana chronically and those who increased their use mid-college utilized health care more often and had higher levels of depressive and anxiety symptoms seven years post-college (Caldeira et al., 2012). This study suggests that some of the individuals that are among the most at risk for long-term negative health outcomes may not be heavy marijuana users or may be nonusers in the beginning of college. It may be im- portant for colleges and universities to continue to screen throughout college for marijuana use as to not overlook students that do not exhibit drug use until later in college. This information can also be useful at the individual level. Students who use marijuana are at risk for health consequences and are likely at greater risk if they increase their use late in college or maintain a pattern of frequent use. These individuals should be targeted for intervention.

Studies have shown that impulsivity and difficulties with mood are robust predictors of drug use among college students and should be targeted in the content of prevention and intervention programs (Conrod, Castellanos-Ryan, & Mackie, 2011). Conrod et al. (2011) developed an intervention program that is designed to target the dif- ferent motivational processes linked to these four personality traits that have been shown to predict alcohol or drug use in adolescents. Individuals who show elevations on one of the four personality risk profiles (hopelessness, anxiety sensitivity, impulsivity and sensation- seeking) go through a tailored two-session coping skills intervention that aims to target the relevant personality and mood factors that may contribute to substance use. Similar interventions could be developed for college student drug users, perhaps as an adjunct to standard BMIs. For example, because the perceived harmfulness of drugs moder- ates the risk of sensation-seeking, interventions that increase aware- ness of the potentially harmful effects of drugs may be a useful intervention component for drug users high in sensation seeking. Sen- sation seekers might also benefit from intervention approaches that help them find alternative experiences to rival or replace drug use. The strong link between mood difficulties and drug use suggests that interventions that incorporate mood regulating strategies may be useful (Geisner et al., 2007, 2004). Murphy, Dennhardt, et al. (2012) and Murphy, Skidmore, et al. (2012) developed a one-session behavioral economic supplement to brief alcohol interventions that attempts to in- crease engagement in constructive goal-directed alternatives to sub- stance use (e.g., academic and campus/community activities). The Substance-Free Activity Session uses motivational interviewing and personalized feedback to enhance the salience of delayed rewards asso- ciated with academic success and includes tailored information on mood management and substance-free social leisure activities (Murphy, Dennhardt, et al., 2012; Murphy, Skidmore, et al., 2012). This approach resulted in significant improvements in drinking

outcomes relative to standard alcohol BMIs, in particular for students with symptoms of depression and low levels of substance-free activi- ties, and may also show promise in the treatment of college drug abuse. There have also been promising results with a group intervention that focused on assertiveness skills (Williams et al., 1983) similar to those included in successful relapse prevention treatments for sub- stance abuse (Carroll & Rawson, 2005).

Another significant challenge for this area of research is to develop in- tervention components that are tailored to drug users, providing both cross-cutting drug treatment elements as well as elements tailored to specific drug types. For example, if a student reports nonmedical use of prescription stimulants with a motive to enhance concentration, a useful intervention component might include training in study skills, whereas if a student reports using marijuana to relieve stress, a component that targets coping skills might prove beneficial. Another challenge is that many students may lack motivation to change marijuana due to the widely held notion that marijuana is relatively benign, and may be un- able to generate “cons” of their marijuana use in the decisional balance exercises often included in BMIs. Elliot, Carey, and Scott-Sheldon (2011) compiled a list of common pros and cons of marijuana use that might be useful to use as a stimulus to prompt students to consider adverse outcomes related to their drug use in the context of a BMI. In- terventions could also combat this perception of marijuana as benign by providing personally tailored and credible information about the specific health, legal, and social risks and consequences associated with the student's pattern of use (e.g., cardiovascular health effects, im- pact on concentration, legal ramifications, driving risk, and academic risk). In particular, the highest risk behaviors should be targeted with harm reduction techniques – even with students who are unwilling to reduce their drug use – including discussing specific strategies to avoid drugged driving, risky sexual behaviors, sharing needles, and accidental overdose (McCambridge & Strang, 2004).

Existing research suggests that marijuana users may benefit from a brief motivational intervention, but more research is necessary to replicate these findings, to determine the relative efficacy of various brief intervention formats and treatment elements, and to identify possible mediators and moderators of treatment outcomes. Studies that examine novel intervention elements that target established risk factors for drug use and drugs other than marijuana may be particularly helpful.

Role of funding sources No grant funding was provided for this study.

Contributors Author AD conducted literature searches and wrote the first draft of the

manuscript. Author JM contributed substantially to subsequent drafts. Both authors have approved the final manuscript.

Conflict of interest All authors declare that they have no conflicts of interest.

Acknowledgements The authors wish to thank Ms. Lindsey Gilbert who assisted in conducting litera-

ture searches for this article.

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